Gist 541f344f0b112c3ace68d676a41682ee

100%

Gist 541f344f0b112c3ace68d676a41682ee

✓ Published0🌍 Public

Last edited Jan 16, 2022

Created on Jan 16, 2022

This example fetches a dataset of Indian Prime Minister Narendra Modi’s speeches from Kaggle using the `kaggle_api_extended.KaggleApi` class, specifically calling `dataset_download_files` to download and unzip the “modi-speeches” dataset. The code then reads the downloaded CSV file into a pandas DataFrame via `pd.read_csv` and extracts the first speech title from the `title` column. The visualization implicitly relies on the Kaggle API and pandas to retrieve and display the dataset’s content, but no charting or rendering library is shown beyond these data-access steps.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 7ef9c17d99cb5a83e7312992e4ec683d

This visualization maps the geographic and thematic reach of Narendra Modi’s speeches, sourced from a Kaggle dataset of text speeches starting in 2018. It displays an interactive choropleth of India, with state boundaries drawn using `d3.geoPath` and a `d3.forceSimulation` layout that positions speech-related keywords as nodes. Hovering over a state reveals its name, while colored circles represent speech themes, with sizes scaled to frequency. The code uses D3.js for SVG rendering and transitions, and it loads the dataset from a JSON file, updating the visualization dynamically.

Aadiamaan92

76% match

Loading thumbnail…

Gist 7014a4039307a88d7d10e9e4adce0549

This example demonstrates an automated GitHub Actions workflow that scrapes speeches by Indian Prime Minister Narendra Modi on a daily schedule. The workflow, defined in a YAML file, runs a Python script named `scrapper.py` at 8 AM UTC using a cron trigger. It sets up Python 3.8, installs dependencies via Poetry, and caches the virtual environment to speed up subsequent runs. The scraper uploads data to Kaggle using credentials stored as GitHub secrets, with the workflow supporting both scheduled and manual execution through `workflow_dispatch`.

Aadiamaan92

70% match

Loading thumbnail…

Fetch Delta using Direct API Calls

This example shows how to incrementally fetch newly published speech titles from the Narendra Modi website using direct API calls. It paginates through the site’s `loadspeeche` endpoint, parsing the returned HTML with `lxml`’s `etree` and XPath queries. The script stops fetching pages once it encounters a title matching the previously saved latest speech, avoiding redundant downloads. It then calls `get_article_data(tree)` on new entries, demonstrating a simple delta-extraction pattern for tracking content updates over time.

Aadiamaan92

64% match

Loading thumbnail…

Gist d5f9fd4d851d95e8815ba77e1364eb5d

This example demonstrates how to update a dataset of speeches by appending new entries and committing the changes to a data repository. It shows the process of merging delta records with existing speech data using pandas, then saving the combined result to a CSV file. The code uses `pd.concat` to combine the DataFrames, `to_csv` to persist the updated file, and the `api.dataset_create_version` method from the data platform’s API to create a new version with a timestamped note. This workflow highlights automated data maintenance and version control for evolving datasets.

Aadiamaan92

63% match

Loading thumbnail…

Gist 7083cd8264b6aa48ce4bbfaf44546f07

This example builds a profile-scraping pipeline for social media data, turning raw CSV input into a structured DataFrame with fields for user names, follower counts, post dates, likes, comments, captions, and industry tags. The code reads `raw_data.csv` via `pandas.read_csv` and then initializes an empty DataFrame with predefined column names, preparing it for subsequent population. The visualization focuses on the data-preparation stage rather than graphical output, demonstrating how pandas handles tabular ingestion and schema definition. No rendering library like d3 or Three.js appears here; instead, the emphasis is on clean data structuring for later analysis or charting.

AArizonaTay

57% match

Loading thumbnail…

Gist bfd5f48411d371e896e1

This visualization maps the speech patterns of 2016 U.S. presidential candidates by scraping and parsing transcripts from the blog What The Folly. The author uses Bash commands with `wget`, `grep`, `sed`, and `awk` to extract article text, then organizes files by candidate. The resulting dataset supports comparative analysis of candidate rhetoric, though the code shown focuses on data collection and file management rather than visual rendering, with no charting libraries or display techniques specified.

BBBischof

56% match

Loading thumbnail…

Gist d2f2bc6be1716820160ed5183ea07eaa

This example shows a browser-based text analytics pipeline that scrapes and processes web content before visualizing topic models. The code first uses Selenium with Chrome WebDriver to scrape data, then applies NLP libraries including NLTK, spaCy, TextBlob, and gensim’s LdaModel for topic modeling and coherence scoring. The final visualization employs Plotly’s `plotly.express` and `plotly.graph_objs` to render interactive charts, with `pyo.init_notebook_mode()` enabling notebook display. The rendering approach is evident from the use of `go` traces and `px` figures, though the specific chart type is not shown in the code snippet. The data source is dynamic web pages, as indicated by the Selenium scraping logic.

AArizonaTay

56% match

Loading thumbnail…

Gist e049bb6d7696d1b2d4ff242e68fcd062

This example analyzes text data by performing topic modeling and visualization. It shows the resulting topics and their associated terms, revealing latent themes in the dataset. The code uses NLTK for tokenization and stopword removal, TextBlob for sentiment analysis, and Gensim’s LdaModel and CoherenceModel for topic extraction. Plotly’s `scatter` and `bar` traces render the interactive visualizations, with data processed from a pandas DataFrame. The rendering approach relies on Plotly’s offline mode, initiated via `pyo.init_notebook_mode()`.

AArizonaTay

55% match